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  <div class="section" id="scorecardmodel-models-package">
<h1>ScoreCardModel.models package<a class="headerlink" href="#scorecardmodel-models-package" title="Permalink to this headline">¶</a></h1>
<div class="section" id="submodules">
<h2>Submodules<a class="headerlink" href="#submodules" title="Permalink to this headline">¶</a></h2>
</div>
<div class="section" id="module-ScoreCardModel.models.logistic_regression_model">
<span id="scorecardmodel-models-logistic-regression-model-module"></span><h2>ScoreCardModel.models.logistic_regression_model module<a class="headerlink" href="#module-ScoreCardModel.models.logistic_regression_model" title="Permalink to this headline">¶</a></h2>
<div class="section" id="logistic">
<h3>logistic回归模型<a class="headerlink" href="#logistic" title="Permalink to this headline">¶</a></h3>
<p>基础分类器,广泛应用在工业领域.</p>
<div class="section" id="id1">
<h4>用法<a class="headerlink" href="#id1" title="Permalink to this headline">¶</a></h4>
<p>由于数据进来格式千奇百怪,这个模型最好的用法是继承后重写
<cite>predict</cite>,`pre_trade`,`pre_trade_batch`这几个方法,适当的也可以重写`train`方法.</p>
<div class="code python highlight-default"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">MyLR</span><span class="p">(</span><span class="n">LogisticRegressionModel</span><span class="p">):</span>
    <span class="k">def</span> <span class="nf">predict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
        <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">pre_trade</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_predict_proba</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>

    <span class="k">def</span> <span class="nf">pre_trade</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
        <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
        <span class="n">result</span> <span class="o">=</span> <span class="p">[]</span>
        <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">x</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
            <span class="n">t</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">ds</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">transform</span><span class="p">([</span><span class="n">v</span><span class="p">])[</span><span class="mi">0</span><span class="p">]</span>
            <span class="n">r</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">woes</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">transform</span><span class="p">([</span><span class="n">t</span><span class="p">])[</span><span class="mi">0</span><span class="p">]</span>
            <span class="n">result</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">r</span><span class="p">)</span>
        <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">result</span><span class="p">)</span>
    <span class="k">def</span> <span class="nf">_pre_trade_batch_row</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span><span class="n">row</span><span class="p">,</span><span class="n">Y</span><span class="p">,</span><span class="n">bins</span><span class="p">):</span>
        <span class="n">d</span> <span class="o">=</span> <span class="n">Discretization</span><span class="p">(</span><span class="n">bins</span><span class="p">)</span>
        <span class="n">d_row</span> <span class="o">=</span> <span class="n">d</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">row</span><span class="p">)</span>
        <span class="n">woe</span> <span class="o">=</span> <span class="n">WeightOfEvidence</span><span class="p">()</span>
        <span class="n">woe</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">d_row</span><span class="p">,</span><span class="n">Y</span><span class="p">)</span>
        <span class="k">return</span> <span class="n">d</span><span class="p">,</span><span class="n">woe</span><span class="p">,</span><span class="n">woe</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">d_row</span><span class="p">)</span>

    <span class="k">def</span> <span class="nf">pre_trade_batch</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span><span class="n">Y</span><span class="p">):</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">ds</span> <span class="o">=</span> <span class="p">{}</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">woes</span> <span class="o">=</span> <span class="p">{}</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">table</span> <span class="o">=</span> <span class="p">{}</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">ds</span><span class="p">[</span><span class="s2">&quot;sepal length (cm)&quot;</span><span class="p">],</span><span class="bp">self</span><span class="o">.</span><span class="n">woes</span><span class="p">[</span><span class="s2">&quot;sepal length (cm)&quot;</span><span class="p">],</span><span class="bp">self</span><span class="o">.</span><span class="n">table</span><span class="p">[</span><span class="s2">&quot;sepal length (cm)&quot;</span><span class="p">]</span><span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_pre_trade_batch_row</span><span class="p">(</span>
            <span class="n">X</span><span class="p">[</span><span class="s2">&quot;sepal length (cm)&quot;</span><span class="p">],</span><span class="n">Y</span><span class="p">,[</span><span class="mi">0</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">5</span><span class="p">,</span><span class="mi">8</span><span class="p">])</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">ds</span><span class="p">[</span><span class="s1">&#39;sepal width (cm)&#39;</span><span class="p">],</span><span class="bp">self</span><span class="o">.</span><span class="n">woes</span><span class="p">[</span><span class="s1">&#39;sepal width (cm)&#39;</span><span class="p">],</span><span class="bp">self</span><span class="o">.</span><span class="n">table</span><span class="p">[</span><span class="s1">&#39;sepal width (cm)&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_pre_trade_batch_row</span><span class="p">(</span>
            <span class="n">X</span><span class="p">[</span><span class="s1">&#39;sepal width (cm)&#39;</span><span class="p">],</span><span class="n">Y</span><span class="p">,[</span><span class="mi">0</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mf">2.5</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mf">3.5</span><span class="p">,</span><span class="mi">5</span><span class="p">])</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">ds</span><span class="p">[</span><span class="s1">&#39;petal length (cm)&#39;</span><span class="p">],</span><span class="bp">self</span><span class="o">.</span><span class="n">woes</span><span class="p">[</span><span class="s1">&#39;petal length (cm)&#39;</span><span class="p">],</span><span class="bp">self</span><span class="o">.</span><span class="n">table</span><span class="p">[</span><span class="s1">&#39;petal length (cm)&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_pre_trade_batch_row</span><span class="p">(</span>
            <span class="n">X</span><span class="p">[</span><span class="s1">&#39;petal length (cm)&#39;</span><span class="p">],</span><span class="n">Y</span><span class="p">,[</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">,</span><span class="mi">5</span><span class="p">,</span><span class="mi">7</span><span class="p">])</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">ds</span><span class="p">[</span><span class="s1">&#39;petal width (cm)&#39;</span><span class="p">],</span><span class="bp">self</span><span class="o">.</span><span class="n">woes</span><span class="p">[</span><span class="s1">&#39;petal width (cm)&#39;</span><span class="p">],</span><span class="bp">self</span><span class="o">.</span><span class="n">table</span><span class="p">[</span><span class="s1">&#39;petal width (cm)&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_pre_trade_batch_row</span><span class="p">(</span>
            <span class="n">X</span><span class="p">[</span><span class="s1">&#39;petal width (cm)&#39;</span><span class="p">],</span><span class="n">Y</span><span class="p">,[</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">])</span>
        <span class="k">return</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">table</span><span class="p">)</span>

<span class="n">lr</span> <span class="o">=</span> <span class="n">MyLR</span><span class="p">()</span>
<span class="n">lr</span><span class="o">.</span><span class="n">train</span><span class="p">(</span><span class="n">l</span><span class="p">,</span><span class="n">z</span><span class="p">)</span>
<span class="n">lr</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">l</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">to_dict</span><span class="p">())</span>
</pre></div>
</div>
<dl class="class">
<dt id="ScoreCardModel.models.logistic_regression_model.LogisticRegressionModel">
<em class="property">class </em><code class="descclassname">ScoreCardModel.models.logistic_regression_model.</code><code class="descname">LogisticRegressionModel</code><a class="reference internal" href="_modules/ScoreCardModel/models/logistic_regression_model.html#LogisticRegressionModel"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#ScoreCardModel.models.logistic_regression_model.LogisticRegressionModel" title="Permalink to this definition">¶</a></dt>
<dd><p>Bases: <a class="reference internal" href="#ScoreCardModel.models.meta.Model" title="ScoreCardModel.models.meta.Model"><code class="xref py py-class docutils literal"><span class="pre">ScoreCardModel.models.meta.Model</span></code></a>, <a class="reference internal" href="ScoreCardModel.mixins.html#ScoreCardModel.mixins.serialize_mixin.SerializeMixin" title="ScoreCardModel.mixins.serialize_mixin.SerializeMixin"><code class="xref py py-class docutils literal"><span class="pre">ScoreCardModel.mixins.serialize_mixin.SerializeMixin</span></code></a></p>
<p>该类最好是继承了使用,继承后重写`predict`和`pre_trade`</p>
<dl class="attribute">
<dt id="ScoreCardModel.models.logistic_regression_model.LogisticRegressionModel.feature_order">
<code class="descname">feature_order</code><a class="headerlink" href="#ScoreCardModel.models.logistic_regression_model.LogisticRegressionModel.feature_order" title="Permalink to this definition">¶</a></dt>
<dd><p><em>Sequence</em> –</p>
<ul class="simple">
<li>特征顺序</li>
</ul>
</dd></dl>

<dl class="attribute">
<dt id="ScoreCardModel.models.logistic_regression_model.LogisticRegressionModel._model">
<code class="descname">_model</code><a class="headerlink" href="#ScoreCardModel.models.logistic_regression_model.LogisticRegressionModel._model" title="Permalink to this definition">¶</a></dt>
<dd><p><em>sklearn.model</em> –</p>
<ul class="simple">
<li>训练出来的sklearn的分类器模型</li>
</ul>
</dd></dl>

<dl class="attribute">
<dt>
<code class="descname">feature_order</code><em class="property"> = None</em></dt>
<dd></dd></dl>

<dl class="method">
<dt id="ScoreCardModel.models.logistic_regression_model.LogisticRegressionModel.pre_trade">
<code class="descname">pre_trade</code><span class="sig-paren">(</span><em>x</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/ScoreCardModel/models/logistic_regression_model.html#LogisticRegressionModel.pre_trade"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#ScoreCardModel.models.logistic_regression_model.LogisticRegressionModel.pre_trade" title="Permalink to this definition">¶</a></dt>
<dd><p>“数据预处理,预测的时候由于输入未必是处理好的,因此需要先做下预处理</p>
</dd></dl>

<dl class="method">
<dt id="ScoreCardModel.models.logistic_regression_model.LogisticRegressionModel.pre_trade_batch">
<code class="descname">pre_trade_batch</code><span class="sig-paren">(</span><em>X</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/ScoreCardModel/models/logistic_regression_model.html#LogisticRegressionModel.pre_trade_batch"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#ScoreCardModel.models.logistic_regression_model.LogisticRegressionModel.pre_trade_batch" title="Permalink to this definition">¶</a></dt>
<dd><p>批量数据预处理,训练的时候由于输入未必是处理好的,因此需要先做下预处理</p>
</dd></dl>

<dl class="method">
<dt id="ScoreCardModel.models.logistic_regression_model.LogisticRegressionModel.predict">
<code class="descname">predict</code><span class="sig-paren">(</span><em>x</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/ScoreCardModel/models/logistic_regression_model.html#LogisticRegressionModel.predict"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#ScoreCardModel.models.logistic_regression_model.LogisticRegressionModel.predict" title="Permalink to this definition">¶</a></dt>
<dd><p>预测用的接口,根据需求重写实现</p>
</dd></dl>

</dd></dl>

</div>
</div>
</div>
<div class="section" id="module-ScoreCardModel.models.meta">
<span id="scorecardmodel-models-meta-module"></span><h2>ScoreCardModel.models.meta module<a class="headerlink" href="#module-ScoreCardModel.models.meta" title="Permalink to this headline">¶</a></h2>
<p>定义分类器模型的抽象基类</p>
<dl class="class">
<dt id="ScoreCardModel.models.meta.Model">
<em class="property">class </em><code class="descclassname">ScoreCardModel.models.meta.</code><code class="descname">Model</code><a class="reference internal" href="_modules/ScoreCardModel/models/meta.html#Model"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#ScoreCardModel.models.meta.Model" title="Permalink to this definition">¶</a></dt>
<dd><p>Bases: <code class="xref py py-class docutils literal"><span class="pre">abc.ABC</span></code></p>
<p>模型的抽象类</p>
<dl class="attribute">
<dt id="ScoreCardModel.models.meta.Model.feature_order">
<code class="descname">feature_order</code><em class="property"> = None</em><a class="headerlink" href="#ScoreCardModel.models.meta.Model.feature_order" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>

<dl class="method">
<dt id="ScoreCardModel.models.meta.Model.pre_trade">
<code class="descname">pre_trade</code><span class="sig-paren">(</span><em>x</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/ScoreCardModel/models/meta.html#Model.pre_trade"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#ScoreCardModel.models.meta.Model.pre_trade" title="Permalink to this definition">¶</a></dt>
<dd><p>向量预处理</p>
</dd></dl>

<dl class="method">
<dt id="ScoreCardModel.models.meta.Model.pre_trade_batch">
<code class="descname">pre_trade_batch</code><span class="sig-paren">(</span><em>x</em>, <em>y</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/ScoreCardModel/models/meta.html#Model.pre_trade_batch"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#ScoreCardModel.models.meta.Model.pre_trade_batch" title="Permalink to this definition">¶</a></dt>
<dd><p>全部数据预处理</p>
</dd></dl>

<dl class="method">
<dt id="ScoreCardModel.models.meta.Model.predict">
<code class="descname">predict</code><span class="sig-paren">(</span><em>x</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/ScoreCardModel/models/meta.html#Model.predict"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#ScoreCardModel.models.meta.Model.predict" title="Permalink to this definition">¶</a></dt>
<dd><p>输入一个特征向量预测</p>
</dd></dl>

<dl class="method">
<dt id="ScoreCardModel.models.meta.Model.train">
<code class="descname">train</code><span class="sig-paren">(</span><em>dataset</em>, <em>target</em>, <em>*</em>, <em>test_size=0.3</em>, <em>random_state=0</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/ScoreCardModel/models/meta.html#Model.train"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#ScoreCardModel.models.meta.Model.train" title="Permalink to this definition">¶</a></dt>
<dd><p>训练一组数据,输入必须是pandas的DataFrame</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>dataset</strong> (<em>pandas.DataFrame</em>) – <ul>
<li>训练用的DataFrame</li>
</ul>
</li>
<li><strong>target</strong> (<em>Option</em><em>[</em><em>str</em><em>,</em><em>pandas.seri</em><em>]</em>) – <ul>
<li>标签数据所在的列</li>
</ul>
</li>
<li><strong>test_size</strong> (<em>float</em>) – <ul>
<li>测试集比例</li>
</ul>
</li>
<li><strong>random_state</strong> – <ul>
<li>随机状态</li>
</ul>
</li>
</ul>
</td>
</tr>
</tbody>
</table>
</dd></dl>

</dd></dl>

</div>
<div class="section" id="module-ScoreCardModel.models">
<span id="module-contents"></span><h2>Module contents<a class="headerlink" href="#module-ScoreCardModel.models" title="Permalink to this headline">¶</a></h2>
<div class="section" id="id2">
<h3>分类器模型<a class="headerlink" href="#id2" title="Permalink to this headline">¶</a></h3>
<p>这个模块就是对sklearn的一些分类器做些封装.主要目的就是为了好序列化.`meta`模块定义了需要实现的接口.
需要注意的是评分卡需要的是一个二分类的分类器.</p>
<p>下面实现的分类器最好是继承了使用,一般需要重写`predict`,`pre_trade`,`pre_trade_batch`这几个方法,
适当的也可以重写`train`方法</p>
</div>
<div class="section" id="id3">
<h3>logistic回归分类器<a class="headerlink" href="#id3" title="Permalink to this headline">¶</a></h3>
<p>最常见的分类器,书上也是用这个.好处是实现方便,运算快.坏处也就是对非线性数据表现比较差.
因此最常见的使用方式就是连续数据离散化.</p>
</div>
</div>
</div>


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  <h3><a href="index.html">Table Of Contents</a></h3>
  <ul>
<li><a class="reference internal" href="#">ScoreCardModel.models package</a><ul>
<li><a class="reference internal" href="#submodules">Submodules</a></li>
<li><a class="reference internal" href="#module-ScoreCardModel.models.logistic_regression_model">ScoreCardModel.models.logistic_regression_model module</a><ul>
<li><a class="reference internal" href="#logistic">logistic回归模型</a><ul>
<li><a class="reference internal" href="#id1">用法</a></li>
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<li><a class="reference internal" href="#module-ScoreCardModel.models.meta">ScoreCardModel.models.meta module</a></li>
<li><a class="reference internal" href="#module-ScoreCardModel.models">Module contents</a><ul>
<li><a class="reference internal" href="#id2">分类器模型</a></li>
<li><a class="reference internal" href="#id3">logistic回归分类器</a></li>
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